RESEARCH PAPER
Application of machine learning and deep learning for the prediction of HIV/AIDS
1
Mizan Tepi University, Tepi, Ethiopia
Submission date: 2021-06-05
Acceptance date: 2021-06-22
Publication date: 2022-01-15
HIV & AIDS Review 2022;21(1):17-23
KEYWORDS
TOPICS
socio-medical aspects of HIV and AIDSeducational aspects of HIV and AIDSpatient-physician relationship
ABSTRACT
Introduction:
Nowadays human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) is very dangerous. HIV targets the resistant system and weakens people's denial against many contaminations and some kinds of cancer. As the virus breaks up and impairs the function of immunity, infected people gradually become immunodeficient. Both deep learning and machine learning models play a great role in the prediction of diseases. The function of immunity is CD4 cell count. In this study both the machine learning and deep learning algorithms were applied.
Material and methods:
Nowadays human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) is very dangerous. HIV targets the resistant system and weakens people's denial against many contaminations and some kinds of cancer. As the virus breaks up and impairs the function of immunity, infected people gradually become immunodeficient. Both deep learning and machine learning models play a great role in the prediction of diseases. The function of immunity is CD4 cell count. In this study both the machine learning and deep learning algorithms were applied.
Results:
Based on the evaluation, deep learning models achieve better results in the metrics of accuracy, precision, and F-score than machine learning models. But in sensitivity metrics machine learning models achieve better result than deep learning. Machine learning algorithms SVM, RF, and NB provide accuracy of 89.00%, 87.00%, and 86.94%; precision of 75.89%, 74.97%, and 75.78%; sensitivity of 87.96%, 84.00%, and 84.12%; F-score of 82.87%, 80.03%, and 79.05%, respectively. LSTM, GRU provides accuracy of 97.65%, 96.00%, precision of 77.35%, 84.00%, sensitivity of 87.93%, 82.98%, and F-score of 82.03%, 83.20%, respectively.
Conclusions:
The possibility survival of the illness is less than no illness. The existence of TB negative is higher than TB positive. In the machine learning model SVM provides better sensitivity with 87.96%, long short-term memory provides accuracy of 97.65%, precision of 77.35%, sensitivity of 87.93%, and F-score of 82.03%.
Nowadays human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) is very dangerous. HIV targets the resistant system and weakens people's denial against many contaminations and some kinds of cancer. As the virus breaks up and impairs the function of immunity, infected people gradually become immunodeficient. Both deep learning and machine learning models play a great role in the prediction of diseases. The function of immunity is CD4 cell count. In this study both the machine learning and deep learning algorithms were applied.
Material and methods:
Nowadays human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) is very dangerous. HIV targets the resistant system and weakens people's denial against many contaminations and some kinds of cancer. As the virus breaks up and impairs the function of immunity, infected people gradually become immunodeficient. Both deep learning and machine learning models play a great role in the prediction of diseases. The function of immunity is CD4 cell count. In this study both the machine learning and deep learning algorithms were applied.
Results:
Based on the evaluation, deep learning models achieve better results in the metrics of accuracy, precision, and F-score than machine learning models. But in sensitivity metrics machine learning models achieve better result than deep learning. Machine learning algorithms SVM, RF, and NB provide accuracy of 89.00%, 87.00%, and 86.94%; precision of 75.89%, 74.97%, and 75.78%; sensitivity of 87.96%, 84.00%, and 84.12%; F-score of 82.87%, 80.03%, and 79.05%, respectively. LSTM, GRU provides accuracy of 97.65%, 96.00%, precision of 77.35%, 84.00%, sensitivity of 87.93%, 82.98%, and F-score of 82.03%, 83.20%, respectively.
Conclusions:
The possibility survival of the illness is less than no illness. The existence of TB negative is higher than TB positive. In the machine learning model SVM provides better sensitivity with 87.96%, long short-term memory provides accuracy of 97.65%, precision of 77.35%, sensitivity of 87.93%, and F-score of 82.03%.
REFERENCES (29)
1.
Yang FN, Hassanzadeh-Behbahani S, Bronshteyn M, et al. Connectome-based prediction of global cognitive performance in people with HIV. Neuroimage Clin 2021; 30: 102677.
2.
Singh Y, Narsai N, Mars M. Applying machine learning to predict patient-specific current CD 4 cell count in order to determine the progression of human immunodeficiency virus (HIV) infection. Afr J Biotechnol 2013; 12: 3724-3730.
3.
Yin Y, Xue M, Shi L, et al. A noninvasive prediction model for hepatitis B virus disease in patients with HIV: based on the population of Jiangsu, China. Biomed Res Int 2021; 2021:6696041.
4.
Bisaso KR, Karungi SA, Kiragga A, Mukonzo JK, Castelnuovo B. A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients. BMC Med Inform Decis Mak 2018; 18: 77.
5.
Bonet I, García MM, Saeys Y, Van de Peer Y, Grau R. Predicting human immunodeficiency virus (HIV) drug resistance using recurrent neural networks. In International Work-Conference on the Interplay Between Natural and Artificial Computation. Berlin, Heidelberg: Springer; 2007, pp. 234-243.
6.
Singh Y. Machine learning to improve the effectiveness of ANRS in predicting HIV drug resistance. Healthc Inform Res 2017; 23: 271-276.
7.
Dubey A. Machine learning approaches in drug development of HIV/AIDS. Int J Mol Biol Open Access 2018; 3: 23-25.
8.
Young SD, Yu W, Wang W. Toward automating HIV identification: machine learning for rapid identification of HIV-related social media data. J Acquir Immune Defic Syndr 2017; 74 Suppl 2: S128-S131.
9.
Gelaw YA, Magalhães RJS, Assefa Y, Williams G. Spatial clustering and socio-demographic determinants of HIV infection in Ethiopia, 2015-2017. Int J Infect Dis 2019; 82: 33-39.
10.
Ramachandran A, Kumar A, Koenig H, et al. Predictive analytics for retention in care in an urban HIV clinic. Sci Rep 2020; 10: 6421.
11.
Shen C, Yu X, Harrison RW, Weber IT. Automated prediction of HIV drug resistance from genotype data. BMC Bioinformatics 2016; 17 Suppl 8: 278.
12.
Marcus JL, Sewell WC, Balzer LB, Krakower DS. Artificial intelligence and machine learning for HIV prevention: Emerging approaches to ending the epidemic. Curr HIV/AIDS Rep 2020; 17: 171-179.
13.
Singh O, Su ECY. Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features. BMC Bioinformatics 2016; 17: 279-289.
14.
Tu W, Chen PA, Koenig N, et al. Machine learning models reveal neurocognitive impairment type and prevalence are associated with distinct variables in HIV/AIDS. J Neurovirol 2020; 26: 41-51.
15.
Singh Y, Mars M. Support vector machines to forecast changes in CD4 count of HIV-1 positive patients. Sci Res Essays 2010; 5: 2384-2390.
16.
Cole JH, Underwood J, Caan MW, et al. Increased brain-predicted aging in treated HIV disease. Neurology 2017; 88: 1349-1357.
17.
Ahlström MG, Ronit A, Omland LH, Vedel S, Obel N. Algorithmic prediction of HIV status using nation-wide electronic registry data. EclinicalMedicine 2019; 17: 100203.
18.
Zazzi M, Incardona F, Rosen-Zvi M, et al. Predicting response to antiretroviral treatment by machine learning: the EuResist project. Intervirology 2012; 55: 123-127.
19.
Nan Y, Gao Y. A machine learning method to monitor China’s AIDS epidemics with data from Baidu trends. PLoS One 2018; 13: e0199697.
20.
Lu X, Wang L, Jiang Z. The Application of Deep Learning in the Prediction of HIV-1 Protease Cleavage Site. In 2018 5th International Conference on Systems and Informatics (ICSAI). IEEE; 2018, pp. 1299-1304.
21.
Steiner MC, Gibson KM, Crandall KA. Drug resistance prediction using deep learning techniques on HIV-1 sequence data. Viruses 2020; 12: 560.
22.
Wang G, Wei W, Jiang J, et al. Application of a long short-term memory neural network: a burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China. Epidemiol Infect 2019; 147: e194.
23.
Rajpurkar P, O’Connell C, Schechter A, et al. CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV. NPJ Digit Med 2020; 3: 115.
24.
Mayr A, Klambauer G, Unterthiner T, Hochreiter S. DeepTox: toxicity prediction using deep learning. Front Environ Sci 2016; 3: 80.
25.
Li X, Xu X, Wang J, Li J, Qin S, Yuan J. Study on prediction model of HIV incidence based on GRU neural network optimized by MHPSO. Ieee Access 2020; 8: 49574-49583.
26.
Madigan EA, Curet OL, Zrinyi M. Workforce analysis using data mining and linear regression to understand HIV/AIDS prevalence patterns. Hum Resour Health 2008; 6: 2.
27.
Oliveira A, Faria BM, Gaio AR, Reis LP. Data mining in HIV-AIDS surveillance system. J Med Syst 2017; 41: 51.
28.
Hajipour M, Taherpour N, Fateh H, et al. Predictive factors of infant mortality using data mining in Iran. Journal of Comprehensive Pediatrics 2021; 12: e108575.
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